AI is a great tool to support your work. Anyone looking to boost their productivity tends to think first about improving their prompts. But small, key changes can have a much greater impact.

Up to 80 percent of AI projects fail. This is not due to the technology - but to the implementation. Here are a few practical tips on how to overcome typical stumbling blocks in AI projects.

“A very good approach.” “Your assessment makes perfect sense.” Anyone who works with AI assistants like ChatGPT or Claude is familiar with phrases like these. They feel good—and that’s exactly the problem. An AI that simply tells users what they want to hear may be pleasant to interact with, but it’s useless as a basis for decision-making!

Vibe coding describes a new approach to software development: AI translates natural language into code. This makes many aspects of product management much easier – but it does not replace one’s own judgment.

The key to sustainable AI success, on the other hand, lies in a simple principle: start small and then, based on your own individual service processes, look at which use cases AI can actually be a relief.

Choosing the right model can be crucial, especially for more complex tasks. Different large language models have different strengths - and sometimes a change of model is the quickest way to achieve the goal.

What is shadow AI and how can companies prevent its use by employees and the associated security risks?

Generative AI gives companies the opportunity to make email processing more efficient, increase customer satisfaction, and reduce the workload of employees.

Large language models (LLMs) are currently outdoing each other not only through better performance, but also through ever larger so-called context windows. But what does that actually mean?

Technological developments are happening one after the other, and this also applies to the further development of LLMs. What used to be text-based language models are now omni-modal.

AI is more than just a tool for quickly answering questions – it can uncover in-depth patterns and thought processes. This requires the right prompts.

If you've ever wanted to know how to give an AI better instructions or ask better questions, we recommend getting help from an AI!

How can chatbots and voicebots be used to access current and internal knowledge? This is where RAG comes into play.

Comprehensive testing is crucial for companies to be able to use large language models (LLMs) safely and effectively. This is because models that are not thoroughly tested can deliver incorrect or biased results.

One urgent security issue at the moment is the implementation of the EU AI Act - and therefore the protection of personal data through anonymization and pseudonymization. Tip: There is technical support for this.